Guided node graph convolutional networks for repository recommendation
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Published:2023-01-30
Issue:1
Volume:27
Page:181-198
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ISSN:1088-467X
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Container-title:Intelligent Data Analysis
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language:
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Short-container-title:IDA
Author:
Tan Guoqiang1, Shi Yuliang12, Wang Jihu1, Li Hui1, Chen Zhiyong1, Wang Xinjun1
Affiliation:
1. School of Software, Shandong University, Jinan, Shandong, China 2. Dareway Software Co., Ltd, Jinan, Shandong, China
Abstract
Knowledge graph (KG) has been widely used in the field of recommender systems. There are some nodes in KG that guide the occurrence of interaction behaviors. We call them guided nodes. However, the current application doesn’t take into account the guided nodes in KG. We explore the utility of guided nodes in KG. It is applied in repository recommendations. In this paper, we propose an end-to-end framework, namely Guided Node Graph Convolutional Network (GNGCN), which effectively captures the connections between entities by mining the influence of related nodes. We extract samples of each entity in KG as their guided nodes and then combine the information and bias of the guided nodes when computing the representation of a given entity. The guided nodes can be extended to multiple hops. We evaluate our model on a real-world Github dataset named Github-SKG and music recommendation dataset, and the experimental results show that the method outperforms the recommendation baselines and our model is much lighter than others.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science
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